• Journal of Digital Convergence
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• v.19 no.7
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• pp.113-127
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• 2021

This study classifies review attributes into ratings, number of comments and image information in online shopping mall to verify their impact on brand attitude and purchase decision and e-WOM intention. Use SPSS 23.0 for frequency analysis, factor analysis and regression analysis. The results showed that review attributes have a positive effect on brand attitudes, purchase decision and e-WOM intention, but the number of comments has not affect on purchase decision. Brand attitude has a positive effect on purchase decision and e-WOM intention. Brand attitude has media effect in the relationship between ratings, image information and purchase decision, and in the relationship between review attributes and e-WOM intention. As these results, consumers don't always like to have a lot of comments. and should allow to focus on high ratings and photo reviews as much as possible when writing reviews.

• The Korean Journal of Franchise Management
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• v.8 no.1
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• pp.31-41
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• 2017

Purpose - As the domestic coffee market is rapidly growing and competition is intensifying, coffee shops need to establish a marketing strategy that grasps the needs and desires of consumers in order to secure a competitive advantage in terms of survival. From this point of view, this study suggests what choice attributes consumers consider when visiting coffee shops, and analyzes the effect of customer choice attributes on franchise and private coffee shops using fsQCA. Research design, data, and methodology - In the present study, we tried to understand the effect of the combination of choice attribute on satisfaction by the type of coffee shop based on the complex system theory, while studying the existing coffee shop choice attribute focuses on the causal relationship. FsQCA is a complementary analytical method between quantitative and qualitative research, and is a method for effectively analyzing the complex combination of causal variables. Result - The results of the study are as follows. First, cleanliness was found to be the most important factor in determining coffee quality, which is the most important factor affecting customer satisfaction. Second, customers who prefer franchise coffee shops seem to be most concerned about atmosphere, menu, cleanliness and price. On the other hand, customers who prefer private coffee shops consider image the most important. Conclusions - The implications of this study are as follows. Overall, coffee shops should manage cleanliness basically regardless of the type of store, but they should manage the choice attributes differently depending on the type of coffee shop. Franchise coffee shops will be able to increase the level of store satisfaction by systematically managing the store atmosphere, menu, cleanliness, and price according to the manual using the advantages of the franchise system. On the other hand, unlike the franchise coffee shops, private coffee shops can operate autonomous stores, so customers can use various marketing mixes to enhance their store image.

• Culinary science and hospitality research
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• v.22 no.4
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• pp.156-169
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• 2016

The purpose of this study is to verify relationships among attributes of ramen package design, ramen image, and chinese customer's choice of ramen. We chose, as a sample, ramen 'sour ramen', 'squid jjambbong', 'tasty ramen', 'noodle beef soup', and 'seasame ramen'. The findings and implications can be summarized as follows. first, while chinese customers chose 'sour ramen' as the most favorable ramen, followed by 'tasty ramen', 'squid jjangbbong', 'noodle beef soup', and 'seasame ramen', for ramen image they most highly evaluated 'sour ramen' followed by 'squid jjangbbong', 'tasty ramen', 'seasame ramen', and 'noodle beef soup'. Second, there is a significant difference in popularity and reliability of quality, but no significant difference in attractiveness and healthiness among most attributes of ramen package design. Third, compared to 'seasame ramen', the popularity and reliability of quality for 'sour ramen', reliability of quality for 'squid jjangbbong', reliabilty of quality, and healthiness for 'tasty ramen' had positive effect on choice, while attractiveness for 'noodle beef soup' had a negative effect on their choice.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.47-55
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• 2024

We spend a lot of time in indoor space, and the space has a huge impact on our lives. Interior design plays a significant role to make an indoor space attractive and functional. However, it should consider a lot of complex elements such as color, pattern, and material etc. With the increasing demand for interior design, there is a growing need for technologies that analyze these design elements accurately and efficiently. To address this need, this study suggests a deep learning-based design analysis system. The proposed system consists of a semantic segmentation model that classifies spatial components and an image classification model that classifies attributes such as color, pattern, and material from the segmented components. Semantic segmentation model was trained using a dataset of 30000 personal indoor interior images collected for research, and during inference, the model separate the input image pixel into 34 categories. And experiments were conducted with various backbones in order to obtain the optimal performance of the deep learning model for the collected interior dataset. Finally, the model achieved good performance of 89.05% and 0.5768 in terms of accuracy and mean intersection over union (mIoU). In classification part convolutional neural network (CNN) model which has recorded high performance in other image recognition tasks was used. To improve the performance of the classification model we suggests an approach that how to handle data that has data imbalance and vulnerable to light intensity. Using our methods, we achieve satisfactory results in classifying interior design component attributes. In this paper, we propose indoor space design analysis system that automatically analyzes and classifies the attributes of indoor images using a deep learning-based model. This analysis system, used as a core module in the A.I interior recommendation service, can help users pursuing self-interior design to complete their designs more easily and efficiently.

• Journal of the Korean Society of Costume
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• v.59 no.4
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• pp.54-66
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• 2009

An empirical study was conducted on fashion cultural products with Korean image, targeting Korean and American consumers. Its purpose was to identify Korean and English consumers' attitude and purchase intention and to compare difference of its influence factor toward fashion cultural products of which design sources were derived from the Korean culture. The quantitative research using on-line questionnaires was targeted at American and Korean consumers. A total of 400 responses were used in the analysis. Results of data analyses using SPSS 13.0, are as following. First, for American consumers, uniqueness-seeking had a positive effect on attitude toward of cultural product with Korean image, followed by service of salespersons, change-seeking, and appearance of salespersons. For Korean consumers, easy accessibility was significantly related to attitude. Second, for American consumers, uniqueness-seeking and appearance of salespersons had a significant influence on the purchase intention of the cultural product with Korean image. For Korean consumers, no attributes were related to purchase intention. This research about fashion cultural products that will compete in the global market presents exploratory information targeting domestic and foreign consumers and will contribute to the strategic aspect of newly growing high-added-value industries.

• Journal of the Korean Society of Costume
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• v.54 no.6
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• pp.25-40
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• 2004

The rainbow has been considered as a perfect representative of color harmony in nature. In this study rainbow's colors include seven spectral colors and changeable colors according to observational angle. This study performed a bibliographical inquiry into rainbow colors and the survey research for classification of rainbow color images in fashion design. First, a bibliographical inquiry includes the definition of rainbow colors, physical formation principles of the rainbow, and its aesthetical attributes and symbolism. Second, this survey classifies rainbow color images in fashion design. The results of this study are as follows: 1. The rainbow was the religious and symbolic object before 17th century, and after that period, the rainbow became an aesthetical object. The main symbolic meanings are similar in eastern and western culture: temporary bridge between two world, divine nature, hope/beauty/richness, war/ death/flood/drought. 2. This survey shows that 6 main factors of rainbow color images in fashion design are 'vigorous', 'colorful'. 'fairy', 'fresh', 'mysterious', 'brilliant'. Rainbow color image in fashion design shows past and futuristic image at the same time. The purpose of this study is to systematized the images theoretical bases which are applied to color expression and of rainbow colors and to find out the development about rainbow theme by designers.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.173-180
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• 2022

In the naked eye observation, the health of livestock can be controlled by the range of activity, temperature, pulse, cough, snot, eye excrement, ears and feces. In order to confirm the health of livestock, this paper uses calf face image data to classify the health status by image shape, color and texture. A series of images that have been processed in advance and can judge the health status of calves were used in the study, including 177 images of normal calves and 130 images of abnormal calves. We used GLCM calculation and Convolutional Neural Networks to extract 6 texture attributes of GLCM from the dataset containing the health status of calves by detecting the image of calves and learning the composite image of Convolutional Neural Networks. In the research, the classification ability of GLCM-CNN shows a classification rate of 91.3%, and the subsequent research will be further applied to the texture attributes of GLCM. It is hoped that this study can help us master the health status of livestock that cannot be observed by the naked eye.

• Journal of Korea Multimedia Society
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• v.10 no.7
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• pp.856-868
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• 2007

We propose an effective method for block classification in a document image. The gray level document image is converted to the binary image for a block segmentation. This binary image would be smoothed to find the locations and sizes of each block. And especially during this smoothing, the inner block heights of each block are obtained. The gray level image is divided to several blocks by these location informations. The SGLDM(spatial gray level dependence matrices) are made using the each gray-level document block and the seven second-order statistical texture features are extracted from the (0,1) direction's SGLDM which include the document attributes. Document image blocks are classified to two groups, text and non-text group, by the inner block height of the block at the nearest neighbor rule. The seven texture features(that were extracted from the SGLDM) are used for the five detail categories of small font, large font, table, graphic and photo blocks. These document blocks are available not only for structure analysis of document recognition but also the various applied area.

• Korean Journal of Remote Sensing
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• v.26 no.3
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• pp.317-324
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• 2010

In recent years, many automatic classification approaches have been employed. An automatic classification method can be effective, time-saving and can produce objective results due to the exclusion of operator intervention. This paper proposes a classification method based on automated training for high resolution multispectral images using ancillary data. Generally, it is problematic to automatically classify high resolution images using ancillary data, because of the scale difference between the high resolution image and the ancillary data. In order to overcome this problem, the proposed method utilizes the classification results of a Landsat image as a medium for automatic classification. For the classification of a Landsat image, a maximum likelihood classification is applied to the image, and the attributes of ancillary data are entered as the training data. In the case of a high resolution image, a K-means clustering algorithm, an unsupervised classification, was conducted and the result was compared to the classification results of the Landsat image. Subsequently, the training data of the high resolution image was automatically extracted using regular rules based on a RELATIONAL matrix that shows the relation between the two results. Finally, a high resolution image was classified and updated using the extracted training data. The proposed method was applied to QuickBird and SPOT-5 images of non-accessible areas. The result showed good performance in accuracy assessments. Therefore, we expect that the method can be effectively used to automatically construct thematic maps for non-accessible areas and update areas that do not have any attributes in geographic information system.

• Journal of Information Technology Applications and Management
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• v.15 no.1
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• pp.1-20
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• 2008

This study aims to identify factors affecting the purchase of Korean IT products in overseas markets. The empirical investigation on Chinese and Japanese, which are major overseas markets of Korean IT products and within Korean wave of influence, ascertains that they have had experiences in purchasing Korean IT products such as MP3, hand held phone, and digital camera. In addition, it was revealed that the country image of Korea and valuation of Korean IT products have been enhanced by Korean wave. The result of multiple regression analysis shows that the purchase of Korean IT products is dependent upon social influence, country image, product attributes, preference on Korean wave, and cultural influence. In the separated hypotheses test of China and Japan, the same factors are found to be significant variables affecting the purchase of Korean IT products but practicality, innovativeness, and norms of consumers are found to be significant only in Chinese market. These results imply what should be considered to expedite the export of Korean IT products. In particular, this study finds different factors affecting the purchase of Korean IT products in different countries.